What problem does private AI solve?
Leakage you cannot see or reverse. Staff paste contracts, customer records and source code into whatever assistant is at hand, which is how shadow AI starts, and the company loses track of where that data went, how long it is kept and who can read it. Client contracts, sector rules and confidentiality duties often forbid exactly that. Private AI turns the question “where did our data go?” into one the company can answer from its own logs. Note the name collision: Private AI is also a vendor brand for redaction tools, which is a different thing.
What are the ways to run private AI?
Four options, each trading capability for control:
| Option | Where your data goes | Control | Model quality | Upkeep |
|---|---|---|---|---|
| Public AI app | Vendor’s shared service | Low | Highest | None |
| Enterprise API, no training on data | Vendor’s service under contract | Contractual | Highest | Low |
| Model in your cloud tenancy | Your cloud account and region | High | High | Medium |
| Self-hosted open-weight model | Your servers or private cloud | Full | Good, improving | High |
Most mid-sized companies land on the middle rows: hosted inference inside their own cloud account, or a provider contract that rules out training and limits retention. Self-hosting is the strongest guarantee and the most expensive one, because the team now owns hardware, serving, patching and evaluation.
Private AI vs public AI: what do you give up?
Some capability and some convenience. The newest frontier models often reach public APIs before they reach private deployments, and a smaller model you run yourself, such as a small language model, needs testing on your own tasks before it replaces one. Private does not mean secure by default: a model in your tenancy still needs access control, logging and a usage policy. The honest comparison is per workload, not per company, which is why most firms run a hybrid.
When does a company actually need private AI?
When a workload touches data that contracts, regulators or clients restrict: health records, legal files, financial data, unreleased product plans. Map those workloads first, decide the data residency each needs, and keep public tools for the low-risk rest. Where the requirement is national control rather than company control, the debate becomes data sovereignty and sovereign AI. Start with a data map, not a hardware order.